Concerns related to data privacy, security, and ethical considerations become more prominent as data volumes continue to grow. In contrast to centralized setups, where all data is accessible at a single location, model-based clustering approaches can be successfully employed in federated settings. However, this approach to clustering in federated settings is still relatively unexplored and requires further attention. As federated clustering deals with remote data and requires privacy and security to be maintained, it poses particular challenges as well as possibilities. While model-based clustering offers promise in federated environments, a robust model aggregation method is essential for clustering rather than the generic model aggregation method like Federated Averaging (FedAvg). In this research, we proposed an autoencoder-based clustering method by introducing a novel model aggregation method FednadamN, which is a fusion of Adam and Nadam optimization approaches in a federated learning setting. Therefore, the proposed FednadamN adopted the adaptive learning rates based on the first and second moments of gradients from Adam which offered fast convergence and robustness to noisy data. Furthermore, FednadamN also incorporated the Nesterov-accelerated gradients from Nadam to further enhance the convergence speed and stability. We have studied the performance of the proposed Autoencoder-based clustering methods on benchmark datasets and using the novel FednadamN model aggregation strategy. It shows remarkable performance gain in federated clustering in comparison to the state-of-the-art.

中文翻译：

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一种基于自动编码器的联合聚类，利用稳健的模型融合策略进行联合无监督学习


随着数据量的持续增长，与数据隐私、安全和道德考虑相关的担忧变得更加突出。与所有数据都可以在单个位置访问的集中式设置相比，基于模型的聚类方法可以在联合设置中成功采用。但是，这种在联合设置中聚类的方法仍然相对未被探索，需要进一步关注。由于联合集群处理远程数据并需要维护隐私和安全性，因此它带来了特殊的挑战和可能性。虽然基于模型的聚类在联合环境中提供了前景，但强大的模型聚合方法对于聚类至关重要，而不是像联合平均 （FedAvg） 这样的通用模型聚合方法。在这项研究中，我们通过引入一种新的模型聚合方法 FednadamN 提出了一种基于自动编码器的聚类方法，该方法是在联邦学习环境中融合了 Adam 和 Nadam 优化方法。因此，提出的 FednadamN 采用了基于 Adam 梯度的第一矩和第二矩的自适应学习率，为嘈杂数据提供了快速收敛和鲁棒性。此外，FednadamN 还整合了 Nadam 的 Nesterov 加速梯度，以进一步提高收敛速度和稳定性。我们研究了所提出的基于自动编码器的聚类方法在基准数据集上的性能，并使用了新颖的 FednadamN 模型聚合策略。与最先进的集群相比，它在联合集群中显示出显著的性能提升。